Coronary artery disease (CAD) is a common type of heart disease and leading cause of death in the United States in both men and women. CAD is often caused when the arteries that supply blood to the heart muscle become hardened and narrowed and thus permit less blood to flow therethrough. When this occurs, the heart muscle cannot get the required blood and oxygen it needs, which can lead to chest pain or a heart attack. According to statistics, each year CAD affects some 16 million people in the U.S., causes approximately 1.2 million heart attacks, and is responsible for over 450,000 deaths.
Early detection techniques and procedures have been developed in order to diagnose CAD and determine whether treatment is necessary. However, current diagnostic techniques are often equivocal, which results in a significant number of low to mid risk patients being unnecessarily admitted from emergency rooms to the hospital for further testing. After further testing, CAD is ruled out for many of these low to mid risk patients, which results in unnecessary costs exceeding $10 billon.
Invasive diagnostic techniques for CAD are known, but they are extremely expensive. One common invasive imaging technique is coronary catheter/angiography (CA). With CA, a catheter is inserted into a person's artery and a contrast agent or dye is injected therein. As the contrasted blood flows through the artery, any narrowing areas of a vessel can be readily seen, which can indicate the presence of plaque. Another known invasive imaging technique is intravascular ultrasound (IVUS), which involves the insertion of an IVUS catheter into a blood vessel. The catheter includes a transducer that emits a beam within the artery to gauge the location of the surrounding vessel. The resulting vessel shape that is determined can reveal the presence of any plaque. Another known invasive technique is optical coherence tomography (OCT), which is an interferometric technique that typically employs near-infrared light to capture three-dimensional images from within the artery to show the existence of any plaque.
Due to the cost and invasive nature of these techniques, non-invasive diagnostic techniques have been developed to assist in determining the existence of CAD. While these non-invasive techniques are less expensive, they have limitations. One such increasingly employed technology is coronary computed tomography angiography (CCTA), which non-invasively obtains anatomic data of the vessel and surrounding structures for evaluation of the severity of artery stenosis. In general, CCTA gathers this data through high resolution cardiac imaging. The resultant imaging allows for the assessment of any luminal narrowing and/or atherosclerotic plaque that can cause the stenosis.
Armed with the CCTA data, there are various methods of assessing a coronary artery lumen for stenosis, including visual assessment and quantitative assessment of the stenosis. These methods can be performed manually, semi-automatically or fully automatically using the CCTA data. Commonly used assessment methods include estimating the narrowed diameter of an artery (luminal diameter stenosis) or the narrowed artery area (luminal area stenosis) to grade the severity of stenosis. Such estimates generally involve defining clinically relevant coronary stenosis based on a predetermined percentage of luminal diameter stenosis or luminal area stenosis, i.e., 50%. Generally, the diagnostic performance of these methods provides good sensitivity and specificity for detecting significant severe stenosis.
However, in cases where intermediate stenosis lesions exist, the specificity and accuracy of this method is lower, despite its high negative prediction value. One known cause for this low specificity is that the luminal diameter stenosis and luminal area stenosis assessment techniques may lead cardiac surgeons to overestimate CAD severity similar to rates reported by cardiologists and radiologists. The overestimation of stenosis severity with CCTA may be affected by the assessment of the luminal diameter within the cross-section image since coronary arteries enlarge in response to athermanous plaque growth, a phenomenon referred to as “remodeling”. The overestimation/underestimation and low specificity of stenosis severity may also be affected by the not uncommon existence of irregular arterial lumen shapes at lesion sites. As such, the luminal diameter assessment technique may misrepresent true lumen narrowing in many instances, which does not solve the issue of unnecessary costs associated with accurately assessing the existence of CAD.
The difficulty with these types of assessment techniques can be illustrated by the schematic diagram of FIG. 1, which depicts four examples (A) through (D) of different stenosis severity each having lesions of different geometries. Each of these examples illustrates a condition with a geometric luminal diameter stenosis of 50%. In other words, each of the examples illustrates an effective reduction in the vessel diameter to 50% at the narrowest point with each example including lesions of different shapes, locations and/or sizes. FIG. 2 exemplarily illustrates how these different examples of 50% luminal diameter stenosis can have different stenosis severities. As shown, examples (A) through (D) of FIG. 1 are each mapped to an illustration of standard regular shaped stenosis, which are assumed to have the same blood flow pressure drop from before the stenosis to after the stenosis. As shown, the examples in the bottom of FIG. 2 have different stenosis severities despite each having the same geometric luminal diameter stenosis. This mapping thus illustrates the inaccuracy that can result when evaluating intermediate stenosis regions that have irregular shapes based on luminal diameter and luminal area techniques.
With recent advancements in blood flow hemodynamics, computational fluid dynamics (CFD) simulations have been successfully utilized to predict blood flow characteristics in arteries such as spatial and temporal variations of flow rate and pressure to assist in diagnosing CAD. FFRct and virtual FFR are two recent examples where CFD has been used to predict fractional flow reserve (FFR), which is defined as the pressure distal to a stenosis relative to the pressure before the stenosis. A significant change in this relative pressure will tend to indicate the presence of CAD, i.e., reduced flow rate. FFR has been recognized as the gold standard for intermediate lesion assessment by the European Society of Cardiology. FFR measurement, however, is an invasive method where a pressure sensitive angioplasty wire is placed directly into the coronary artery through coronary catheterization. The FFRct method is complicated as it uses computational modeling on the CCTA for the whole coronary artery tree including a segment of the aorta artery. The virtual FFR method is similarly complicated as it uses computation modeling on the rotational coronary angiography (RoCA) images for the whole major vessel.
Both the FFRct and virtual FFR methods attempt to simulate blood flow in physiologically realistic terms. This requires the estimation of sophisticated boundary conditions and initial conditions, which can be difficult to estimate accurately. In addition, computational modeling requires significant resources (both computational and labor) to segment the whole coronary artery tree domain or the whole vessel domain to construct the patient specific arterial lumen geometry as input to the CFD. This also requires a large domain of patient specific arterial models in order to perform simulations close to the physiological environment to accurately predict FFR. The requirement of a large domain has various disadvantages. One disadvantage is that CFD simulation needs large computational resources as it is a computational insensitive algorithm. Another disadvantage is that the large domain requirement limits the number of CCTA scans that may be used due to the localized low image quality on a normal vessel. Additionally, imaging artifacts such as blurring of motion under limited temporal resolution of imaging, blooming artifacts from calcified plaques under limited spatial resolution of imaging, or even localized noise are more likely to appear in a large domain. This, in turn, reduces the confidence level of the prediction of CAD by the CFD simulation for FFR value.
Due to the limitations with the above treatment methods, the Applicant developed a new technique for assessing stenosis severity. This technique which is the subject of U.S. Pat. No. 8,831,315 is a non-invasive process that involves generating a three dimensional model of one or more lesions of interest based on anatomical imaging of a patient. The model that is constructed of the vessel with lesions is limited to a vessel with a single inlet and one or more outlets. The technique also involves creating a series of comparative two dimensional lesion specific models having conditions that correspond to the generated three dimensional model. Each of the comparative two dimensional models represents a vessel having one or more regular shaped lesions within the vessel, which lesions are differently configured so that each two dimensional vessel model represents a different known level of stenosis severity. The three dimensional model, which is based on user anatomical data, is then mapped to the appropriate two dimensional model to determine a quantitative measure of stenosis severity of the patient's CAD. This technique has been highly useful for simple stenosis morphology in a vessel with only a single segment. This technique, however, has limitations for more complex morphologies of lesions as the accuracy may be affected by the selected flow path despite the fact that the technique has compensated for area difference between the inlet to outlet by using the inlet and outlet radii.